Скачать или смотреть Solving the Elasticsearch Wildcard Search Issue with Numbers in Strings

Discover how to effectively use the `wildcard` query in Elasticsearch when dealing with string fields that contain numbers.
---
This video is based on the question https://stackoverflow.com/q/77257682/ asked by the user 'Jacky Guo' ( https://stackoverflow.com/u/17563102/ ) and on the answer https://stackoverflow.com/a/77257826/ provided by the user 'Val' ( https://stackoverflow.com/u/4604579/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Elasticsearch wildcard does not work when these is a number in the string

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Wildcard Issue in Elasticsearch Searches

If you are using Elasticsearch for document storage and retrieval, you may have encountered issues with using wildcard queries—especially when your data contains numbers in the strings. This can lead to unexpected results where your expected queries return no results at all.

In this guide, we will explore a specific scenario involving a document stored in Elasticsearch and how to resolve the issue with searching for fields that include numbers as part of their string values.

The Problem

Consider a document in an Elasticsearch index called jobpipeline. You might have a field called touchTriggers which stores values that include both letters and numbers, like uc4.abc.done.

When executing a wildcard search query like this:

[[See Video to Reveal this Text or Code Snippet]]

You receive no results, even though the document containing that value exists. However, if you adjust your query to remove the numbers, like so:

[[See Video to Reveal this Text or Code Snippet]]

You do get a response. This discrepancy can cause frustration, especially if you rely heavily on wildcard searches for retrieving data.

The Solution

The key to resolving this issue lies within how Elasticsearch interprets the data types in your mapping. In this case, you need to specify the correct field type that the wildcard query should target.

Targeting the Keyword Field

Elasticsearch treats text fields differently from keyword fields. The touchTriggers field in your mapping is set as a text type which is analyzed and tokenized, meaning the wildcard search you performed wouldn't work as expected.

To get your expected results, you should run the wildcard query on the touchTriggers.keyword field as shown in the following code snippet:

[[See Video to Reveal this Text or Code Snippet]]

Why This Works

Text vs Keyword: The text type is meant for full-text search; it tokenizes the string and performs analysis. In contrast, the keyword type is designed for structured content; it doesn't undergo the same tokenization, preserving the original string as is.

Exact Matches: Searching on the keyword field allows you to perform exact matches or wildcard searches without losing any part of the string.

Conclusion

When you encounter issues with wildcard searches where numbers are involved in strings, always ensure that your queries are targeting the correct field within your mapping.

By directing your wildcard search to the keyword sub-field, you will efficiently retrieve the expected documents containing the specific strings you are searching for.

Final Notes

If you continue to face issues even after these adjustments, double-check your mapping and indexing strategies. It might also be beneficial to review how you are analyzing your data during ingestion to ensure that you capture the desired information effectively.

With these insights, you can enhance your search capabilities within Elasticsearch and make your querying process smoother and more effective.